Pisa syndrome (PS) is a frequent postural complication of Parkinson's disease (PD). PS poorly responds to anti-parkinsonian drugs and the improvement achieved with neurorehabilitation tends to fade in 6 months or less. Transcranial direct current stimulation (t-DCS) is a non-invasive neuromodulation technique that showed promising results in improving specific symptoms in different movement disorders.This study aimed to evaluate the role of bi-hemispheric t-DCS as an add-on to a standardized hospital rehabilitation program in the management of PS in PD.n = 13) for 5 daily sessions (20 min−2 mA) with bi-hemispheric stimulation over the primary motor cortex (M1), or ii) sham stimulation  with the same duration and cadence. At baseline (T0), end of rehabilitation (T1), and 6 months later (T2) patients were evaluated with both trunk kinematic analysis and clinical scales, including UPDRS-III, Functional Independence Measure (FIM), and Numerical Rating Scale for lumbar pain.This study included 28 patients with PD and PS  who underwent a 4-week intensive neurorehabilitation treatment and were randomized to receive: i) t-DCS , lateral trunk inclination (p = 0.013) during upright standing position, total range of motion of the trunk (p = 0.012), FIM score (p = 0.048), and lumbar pain intensity (p = 0.017).When compared to the sham group, the t-DCS group achieved a more pronounced improvement in several variables: overall posture (Our data support the use of neuromodulation with t-DCS as an add-on to neurorehabilitation for the treatment of patients affected by PS in PD. Pisa syndrome (PS) is a frequent postural complication of Parkinson's disease (PD), leading to higher motor and functional disability, gait impairment, higher occurrence of falls, lumbar pain, and cognitive impairment –4.The main feature of PS is the lateral flexion of the trunk. This may be associated with a slight ipsilateral rotation of the trunk around the sagittal axis that leads to a higher and anterior position of the shoulder contralateral to the side of trunk inclination –6.Pisa syndrome is a challenge for physicians as it poorly responds to antiparkinsonian drugs and only transiently improves with neurorehabilitation and botulinum toxin , 7, 8. DSeveral pathophysiological mechanisms have been hypothesized for the genesis of PS in PD, with central and peripheral mechanisms being involved . The cenIt is worth noting that the central hypothesis is supported by animal and human data. Stimulation of the striatum in animal models  induced a trunk inclination contralateral to the stimulated striatum , 15. By These pre-clinical observations are consistent with clinical evidence. PS is more prevalent in PD patients with high motor asymmetry, and in the majority of patients, the side of PS inclination is ipsilateral to the less affected side at PD onset , 3, 6. UTranscranial direct current stimulation delivered over the primary motor cortex (M1) has been tested in several movement disorders with interesting, although sometimes conflicting, results . In idioIn this randomized, sham-controlled study, we aimed to evaluate the efficacy of a bi-hemispheric t-DCS as an add-on to a standardized in-hospital rehabilitation program in the management of PS in PD. The primary outcome of the study was the improvement of overall trunk posture in the upright standing position at the end of the rehabilitation.This study involved 28 patients affected by PD and PS who were consecutively enrolled among those attending the Neurorehabilitation Department of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation  between January 2018 and August 2020. No patients withdrew from the study.Idiopathic PD was diagnosed according to the Movement Disorders Society's clinical diagnostic criteria for PD . PS was The study was a randomized, double-blind, controlled trial that aimed to assess the efficacy of five daily sessions of bi-hemispheric t-DCS in add-on to an in-hospital rehabilitation protocol in patients affected by PS and PD.At hospital admission (T0), the patients underwent a complete neurological examination, a baseline kinematic analysis of trunk movement, and the administration of a set of clinical scales.Patients were then randomly assigned to “t-DCS” or “sham” treatment according to a block randomization method  and referred to as the t-DCS group and the sham group, respectively.The randomization procedure was performed by the local Biodata Center, whose staff was not involved in the study procedures. The Biodata Center staff communicated the randomization code directly to the technician responsible for the t-DCS-related procedures, who was not involved in the subsequent part of the protocol.A unique randomization list was generated before the start of the enrolment. The randomization code remained in the hospital during the study and was not available to the investigators until the study was completed and data were analyzed. The randomization code was stored in a light-sealed envelope and might be broken in case of need for patient safety. There was an envelope for each patient so that the overall randomization code was not revealed in case an envelope was broken.We used a bi-hemispheric t-DCS approach according to previously published protocols , 30.Parallel to neuromodulation, all patients were treated with a standardized 4-week rehabilitation program with 6 sessions per week lasting 90 min each .The kinematic analysis of trunk movement and the administration of the set of clinical scales were repeated at the end of the rehabilitation program (T1) and 6 months later (T2) .All patients received their individualized anti-parkinsonian drug therapy, whose dosing scheme was kept stable during the whole study period.In all patients, PS was not responsive to the anti-parkinsonian drug therapy and its severity did not fluctuate during the day, as we clinically confirmed during the 4-week hospital observation period.www.clinicaltrials.gov (NCT04620863). The authors agree to share anonymized data from this analysis upon reasonable request from qualified investigators. The study was completed in September 2020.The local ethics committee approved the study (p-20190052462), and all participants signed a written informed consent before enrolment. The trial was registered at via a specific battery-driven direct current stimulator  by an expert technician (VG) who was not otherwise involved in the management of the patients. The current was transferred by a saline-soaked pair of surface sponge electrodes (3 × 3 cm for both anode and cathode). t-DCS was always administered in the morning between 8:00 a.m. and 12:00 p.m. The stimulation was delivered within 3 hours after the first L-dopa administration of the day during the ON phase.Neuromodulation was delivered All the participants received daily sessions for 5 consecutive days (20 min per session with a 2 mA intensity). The stimulation started from the first Monday after hospital admission, indeed it was always started in the first seven days of rehabilitation. The primary motor cortex (M1) was identified using the International 10–20 system for C3 (left M1) or C4 (right M1).We used a bi-hemispheric t-DCS approach according to previously published protocols , 30:cathodal stimulation over M1 contralateral to the side of trunk inclination (inhibition of the more active hemisphere/striatum);anodal stimulation over M1 ipsilateral to the side of PS (potentiation of the more denervated hemisphere/striatum).In the sham group, patients underwent the same number of sessions that lasted 20 minutes, but the stimulation intensity was set according to a ramping up/ramping down method and delivered only in the first and last 30 seconds of each session , 32. ThiKinematic analysis of trunk was performed with a 4-camera optoelectronic system  , 7. All The absolute deviation and the range of motion (ROM) of the C7-Sacrum segment (C7-Sa) were calculated according to a previously validated two-landmark system  2, 7)., 7.2, 7)The following parameters were recorded: lateral trunk inclination in the upright standing position (Stat Bend); anterior trunk flexion in the upright standing position (Stat Flex); total postural alteration in the upright standing position (Stat Tot: Stat Bend + Stat Flex); ROM of trunk bending ipsilateral to the side of trunk deviation (ROM Ips); ROM of trunk bending contralateral to the side of trunk deviation (ROM Con); ROM of anterior trunk flexion (ROM Flex); ROM of posterior trunk extension (ROM Ext); and total ROM of the trunk (ROM Tot: ROM Ips + ROM Con + ROM Flex + ROM Ext).Parkinson's disease-related motor disability was assessed through the Unified Parkinson's Disease Rating Scale—Part III (UPDRS-III) . The itewww.openepi.com and based on the difference between the t-DCS and sham groups in the Stat TOT at T1 . The computation was made with the following parameters: CI (two-sided): 95%; power: 80%; ratio of sample-size: 1:1; mean difference: 15; and SD: 10. The minimum sample size suggested was 24 patients (12 patients per group).The sample size was calculated with the online platform SD,” while categorical data were presented as “number (in percentage).” For the statistical analysis, the data were normalized to a 100% baseline and expressed at T1 and T2 as percentage modifications.The SPSS version 21  was used for all the computations. The Kolmogorov-Smirnov test confirmed a normal distribution of our data. Continuous variables were presented as “mean ± t-test for independent samples. Statistical association among categorical variables was tested with Pearson χ2 test or Fisher exact test, if appropriate.Between groups, a comparison was performed with a Student's post hoc Bonferroni's correction for intra-group comparisons. If a significant interaction TIMExSTIM was found, a post hoc analysis was separately performed for the t-DCS group and the sham group.The main analysis was performed with mixed-model ANOVA with two fixed factors: TIME (as in time) (within-subjects factor with 3 levels: T0 vs. T1 vs. T2) and STIM (as in stimulation) (between-subjects factor with 2 levels: t-DCS group vs. sham group), followed by a The primary endpoint was the difference between groups in the percentage modification of Stat Tot at T1 and T2. Stat Tot was selected as the primary outcome measure because it may reflect more comprehensively the full range of improvement of trunk alignment associated with our experimental rehabilitative intervention. Secondary endpoints were the following: Stat Bend; ROM Tot; UPDRS-III; item 3.13 Posture of the UPDRS-III scale; FIM; NRS.All other endpoints  were considered exploratory. To rule out a significant association between the clinical and demographic features and the primary outcome, we performed an exploratory ANOVA for repeated measures test with factor TIME and factors such as gender, type of PD at onset, the most affected side of PD at onset, and the side of trunk inclination. Exploratory analyses are provided in The level of significance was set at α = 0.05 and always corrected for multiple comparisons where appropriate.n = 13, 9 men, 71.9 ± 5.2 years old) and sham group  were comparable for clinical and demographic features .In the subset of patients with unilateral or asymmetric motor symptoms at PD onset , the lateral trunk inclination was toward the less affected side in 12 (57.1%) patients, of which 4 (44.4%) were in the t-DCS group and 8 (66.7%) were in the sham group  improved after rehabilitation in the overall study population (TIME: p = 0.001), being more pronounced in the t-DCS group (STIM: p = 0.014). At post hoc intra-group analyses (TIMExSTIM: p = 0.037), the Stat Tot parameter was not modified in the sham group (p = 0.14). By contrast, in the t-DCS group, Stat Tot was reduced both at T1 (p = 0.001 vs. T0) and at T2 (p = 0.001 vs. T0) when compared to baseline . The reduction of Stat Bend was more pronounced in the t-DCS group (STIM: p = 0.013). The post hoc intra-group analysis (TIMExSTIM: p = 0.036) showed that the Stat Bend parameter was not modified in the sham group (p = 0.375); by contrast, in the t-DCS group the lateral trunk inclination was reduced at T1 (p = 0.001 vs. T0), but the improvement was not retained at T2 (p = 0.118 vs. T0) ( vs. T0) .p = 0.41), and in 4 (30.8%) patients of the t-DCS group and in 1 (6.7%) patient in the sham group at T2 (p = 0.153).The lateral trunk inclination fell below the 10° diagnostic cut-off in 5 (38.5%) patients of the t-DCS group and in 3 (20%) patients of the sham group at T1 . A different behavior over time was described between groups . Specifically, in the t-DCS group, we found an improvement at T1 (p = 0.003 vs. T0), which was not retained at T2 (p = 1.000 vs. T0), while no improvement was detected in the sham group at either time point (The me point .p = 0.061), motor disability (UPDRS-III: p = 0.52), and postural alteration as measured by posture-specific item 3.13 of the UPDRS-III scale (p = 0.79)  .p = 0.638). Pain severity was comparable between groups (NRS: p =0.758) (Lumbar pain was reported in 7 (53.8%) patients in the t-DCS group and in 8 (53.3%) in the sham group ( =0.758) .p = 0.001 vs. T0) but not at T2 (p = 0.056 vs. T0) in the overall population (TIME: p = 0.001), without differences between the t-DCS and sham groups   .p = 0.019), but not at T2 (p = 0.355)  .p = 0.001) at T1 and also at T2, with a greater increase in the t-DCS group (STIM: p = 0.048)  .p = 0.105). In the subgroup of patients reporting lumbar pain at all time-points (7 in both groups), the improvement in pain scores was more pronounced in the t-DCS group when compared to the sham group (STIM: p = 0.017). Remarkably, the persistence in NRS reduction was different between groups (TIMExSTIM: p = 0.035) and still significantly lower at T2 (p = 0.001 vs. T0) only in the t-DCS group . All patients tolerated well the stimulation and no side effects were reported.Finally, 7 out of 13 patients (53.8%) in the t-DCS group and 8 out of 15 patients (53.3%) in the sham group believed that they received active treatment  was indeed more pronounced in the t-DCS group at the end of the rehabilitation program. Similarly, the increase in the global ROM of the trunk was more pronounced in the t-DCS group at T1. The postural and mobility trunk improvement was coupled with a reduction of motor disability at T1 in the overall population, and an increase in FIM score at both T1 and T2 was more pronounced in the t-DCS group. Although the percentage of patients reporting lumbar pain was not reduced, pain severity was lower and the benefit persisted longer in the t-DCS group.The efficacy of bi-hemispheric t-DCS has been previously reported in focal dystonia , 36, 37,The following mechanisms were hypothesized the explain the circuitries involved in basal ganglia modulation after M1 neuromodulation: i) an increase/decrease in M1 excitability may modulate the pallido-thalamocortical pathway, and ii) the dopamine release in the basal ganglia after M1 anodal cortical stimulation may potentiate/inhibit the glutamatergic cortico-striatal pathway –13. In tAs previously mentioned, several case series suggest how DBS may exert positive effects on PS, but its invasiveness and the lack of specifically designed and well-powered clinical trials have limited its application in clinical practice , 19, 20.Only six studies were specifically designed to test the efficacy of non-invasive therapies  in patients with PS and PD , 40–42, Some limitations must be acknowledged for a comprehensive interpretation of our results. First, the small sample size of the present study does not allow us to infer definitive conclusions. In addition, based on our inclusion/exclusion criteria, we enrolled a population without cognitive impairment and without the compelling need for frequent adjustments of the anti-parkinsonian treatment, which further reduced our cohort. Yet, the adaptability of t-DCS is one of its intrinsic limits, wherein although we adopted a bi-hemispheric approach, we cannot exclude that other stimulation paradigms might induce comparable or even better results. For instance, a longer t-DCS stimulation during the hospital setting may induce more pronounced or more persistent effects. Nonetheless, a different study paradigm, namely t-DCS alone or delivered immediately before or after the rehabilitative period, may exert different pathophysiological effects.Despite these limitations, this study warrants further trials on larger cohorts, in order to confirm our findings.In conclusion, t-DCS potentiates the effects of neurorehabilitation in the management of PS in PD. Our data support the use of a bi-hemispheric t-DCS approach as an add-on to neurorehabilitation in the management of patients affected by PS and PD. t-DCS may represent a therapeutic alternative, being a non-invasive, well-tolerated, easily repeatable, and low-cost technique.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Local Ethics Committee of Pavia (p-20190052462). The patients/participants provided their written informed consent to participate in this study.RD: study concept or design, analysis or interpretation of data, writing of the first draft, and RDI takes responsibility for the integrity of the data and the accuracy of the data analysis. APu: acquisition of data, analysis or interpretation of data, drafting, and revision of the manuscript for content. MAl, MAv, GC, and FV: patients enrolment, drafting, and revision of the manuscript for content. CD and SC: patients enrolment and acquisition of data. DM: patients enrolment, acquisition of data, drafting, and revision of the manuscript for content. VG: responsible for t-DCS related procedures. MF: acquisition and analysis of kinematic movement analysis data. VB: acquisition and analysis of data, drafting, and revision of the manuscript for content. EA, GS, and APi: critical analysis of findings and review of the manuscript. CT: study concept or design, drafting, and revision of the manuscript for content. All authors contributed to the article and approved the submitted version.This study was funded by the Italian Ministry of Health (Ricerca Corrente 2018–2020).RD, APu, MAl, MAv, CD, DM, SC, VG, MF, VB, GC, FV, GS, and EA report no funding in the preceding 12 months. APi holds grants that are not related to the subject of the present study, and he reports no biomedical financial interests or potential conflicts of interest. CT received honoraria for their participation in advisory boards or for oral presentations from Allergan, ElectroCore, Eli-Lilly, Novartis, and Teva. CT has no ownership interest and does not own stocks of any pharmaceutical company. CT serves as Chief Section Editor of Frontiers in Neurology—Section Headache Medicine and Facial Pain and on the editorial board of The Journal of Headache and Pain.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Being able to independently determine vaccine induced antibody responses by minimal-invasive methods is of great interest to enable a flexible and effective vaccination strategy. This study aimed to evaluate (1) the accuracy, feasibility, usability and acceptability of capillary blood and saliva self-sampling to determine SARS-CoV-2 antibody responses in patients with immune-mediated inflammatory diseases (IMIDs) and health professionals (HP).p > 0.05), and a small bias or 95% of tests within 10% difference through Bland-Altman. Participants completed a survey to investigate self-sampling usability  and acceptability . Study personnel monitored correct self-sampling completion and recorded protocol deviations.IMID patients and HP having received two doses of SARS-CoV-2 vaccines, self-collected capillary blood (Tasso+) and saliva samples. Capillary samples were considered interchangeable with venous blood if three criteria were met: Spearman's correlation coefficient (r) > 0.8, non-significant Wilcoxon signed-rank test . Capillary self-sampling was perceived as significantly (p < 0.001) less painful compared to traditional venous blood collection. Participants reported a NPS for capillary and saliva self-sampling of +68% and +63%, respectively. The majority of both groups (73%) preferred capillary self-sampling over professional venous blood collection.60 participants (30 IMID patients and 30 HP) were analyzed. We observed interchangeability for capillary samples with an accuracy of 98.3/100% for Anti-SARS-CoV-2 IgG/IgA antibodies, respectively. Fifty-eight capillary blood samples and all 60 saliva samples were successfully collected within the first attempt. Usability of both self-sampling procedures was rated as excellent, with significantly higher saliva ratings (Our results indicate that capillary self-sampling is accurate, feasible and preferred over conventional venous blood collection. Implementation could enable easy access, flexible vaccination monitoring, potentially leading to a better protection of vulnerable patient groups. Self-collection of saliva is feasible and safe however more work is needed to determine its application in clinical practice. Evaluation of an adequate vaccination response and appropriate revaccinations are essential to counteract waning of humoral immune response  and to eIdeally, samples to investigate vaccine immunogenicity could be self-collected at home, and having to travel to healthcare facilities including the burden and infection risk, would be obsolete. Self-sampling enables independent, flexible collection of specimen, such as capillary blood  and saliTo the best of our knowledge, no study has yet directly compared capillary and saliva self-sampling in IMID patients and HP. Therefore, this study aimed to evaluate the accuracy, feasibility, usability and acceptability of capillary blood and saliva self-sampling to determine Anti-SARS-CoV-2 antibody responses in IMID patients and HP.This study was a prospective, single-center, cross-sectional, matched case-control study , see Participants first completed a questionnaire querying previous self-sampling experience and current attitude. After receiving written instructions, participants independently completed an upper-arm-based capillary and saliva specimen collection under the supervision of local study personnel. Additionally patients were presented a video instruction for the capillary self-sampling device. Deviations from the respective self-sampling protocol were recorded. After a traditional venous blood collection, representing the gold-standard, participants completed a final questionnaire to investigate perceived pain during blood collection and a potentially changed attitude toward self-sampling.The agreement of anti-SARS-CoV-2 IgG and IgA antibody levels between matched capillary, saliva and venous samples was the primary outcome. Feasibility was assessed by the number of successfully collected samples within the first attempt. Usability of sampling devices was assessed via the ten-item System Usability Scale (SUS) . SUS scoCapillary samples were collected using the upper-arm based Tasso+ device  and saliva samples were collected using Salivetten Cotton Swab  by spitting directly into the tube without utilizing the cotton swab .The Tasso+ device is attached to the upper arm by an adhesive and the lancet is activated by pressing a button. Prior to capillary blood collection, patients were instructed to warm the chosen collection site for 1 min by applying a heat-pad (L x W x H) 135 x 95 x 25 mm, max. heat 55°C,  to increase local blood flow. Blood is then automatically collected using a vacuum. Participants were instructed to remove the device after a maximum collection time of 5 min or as soon as the collection tube was entirely filled with blood. Participants were instructed to collect a target volume of saliva up to a line mark. Participants should not drink or eat 30 min prior to saliva collection. Matched venous blood samples were collected by trained phlebotomists from all participants within 30 min of capillary blood and saliva collection.Uncentrifuged capillary samples and centrifuged venous blood reference samples were sent by regular mail using standard postage and UN3373 compliant packaging to Thermo Fisher Scientific research laboratory in Freiburg, Germany. Samples were inspected independently by two lab technicians for quality. Upon arrival in the laboratory the samples were processed, resulting serum was transferred into Sarstedt™ 2 ml Polypropylene Micro Tubes  and stored at −20°C until analysis. Saliva samples were stored at the hospital at −20°C and then sent to Thermo Fisher Scientific research laboratory in Freiburg, Germany on dry ice and stored at −20°C until analysis. Prior to testing saliva samples were transferred to a new salivette tube so that all liquid was absorbed by the cotton pouch, followed by a 5 min, 4°C, 3,000 g centrifugation step. The eluate was collected and stored at −20°C. Saliva samples with ≥100 μl eluate volume were suitable for measurement on a Phadia 250 System.TM wells and processed . SARS-CoV-2 Spike 1 (S1) antigen  was adsorbed onto irradiated polystyrene EliArocessed , 19. An We adopted the sample size of previous self-sampling studies , 12 and p < 0.05 for all statistical tests. The distribution of the pairs of variables, and of the difference between two pairs of variables, was assessed with normality tests . When the distribution of the pairs of variables did not follow a Gaussian distribution, non-parametric statistical tests were applied . The Wilcoxon signed-rank test was used to compare the System Usability total Score (SUS) between capillary and saliva self-sampling and within the groups , when the assumptions for a paired t-test were not met. All analyses were completed using the R software environment (R version 4.1.1).Study group characteristics were summarized using appropriate descriptive statistics. Agreement between the two blood collection methods was assessed using a combination of three tests: Paired Wilcoxon signed rank test, correlation analysis, and Bland-Altman analysis. Clinical interchangeability between the two methods was a priori defined following the methodology by Nwankwo et al. : Non-sigA total of 61 participants  were screened for eligibility . One pat. IgG and IgA demonstrated an excellent correlation (rs = 0.99), non-significant Wilcoxon signed-rank test , a small bias  and the majority of measurements were within a 10% difference , see We observed an accuracy of 98.3% (57/58) for anti-SARS-CoV-2 IgG antibodies and 100% (58/58) accuracy for anti-SARS-CoV-2 IgA antibodies, as most of the capillary blood samples fell in the same positive and negative categories as the venous results. Only one variation was observed, where the venous serum value for anti-SARS COV-2 IgG antibodies (6.7 U/ml) was close to the equivocal range of 7 to 10 U/ml and the value measured in the capillary sample (10.5 U/ml) and was just above the cut-off of 10 U/ml. A priori criteria to demonstrate interchangeability to venous blood were also met by capillary blood-based SARS-CoV-2 IgG and IgAThe device, with which the saliva measurements were performed was not completely developed at the time of this study, and the values were not directly comparable see .p < 0.001), see Usability of both self-sampling procedures was rated as excellent, with significantly higher saliva SUS total scores in both groups, resulting in total SUS scores of 95.9 ± 5.7 vs. 90.4 ± 9.7 (The percentage of NPS promoters (NRS 9-10), was similar for both devices , rangingp < 0.001) less painful compared to traditional venous blood collection . Sixty-three point three percentage and 36.7% of IMID patients perceived capillary self-sampling as less or equally painful compared to venous blood collection. In the HP group 53.3, 36.7 and 10.0% perceived capillary self-sampling as less, equally or more painful compared to venous blood collection.Capillary self-sampling was perceived as significantly  stated to have adhered to not eating or drinking 30 min prior to saliva collection. 5/60 (8.3%) participants had to be reminded to remove the cotton from the test tube and 3/60 (5.0%) needed assistance to do that. There was uncertainty among 7/60 (11.67%) participants if the small test tube could be thrown in the trash or not. 7/60 (11.67%) participants were unsure when assessing if enough saliva was collected, especially since saliva was often foamy.One patient  and one HP  failed to collect capillary blood. Both participants stated to be in a hurry, did not pay adequate attention to the instructions and failed to adequately attach the self-sampling device. 17/60 (28.3%) participants did not follow the protocol steps (e.g. wanted to self-sample before attaching the collection tube). 11/60 (18.3%) participants had to be reminded to start the timer while applying the heat pad to the selected spot on the upper arm. Most problems occurred using the heat pad, where 2/60 (3.3%) pointed out that the heat was getting uncomfortable, and one participant stopped the application prematurely. Additionally, 4/60 (6.7%) participants did not understand how to apply the heat pad, 9/60 (15.0%) participants needed assistance with the activation of the heat pad and in 12/60 (20.0%) cases the heat pad was malfunctioning and had to be replaced. About 7/60 (11.7%) did not carry out the disinfection correctly . 5/60 (8.3%) had difficulties with removing the protective foil. Two participants accidentally teared the adhesive foil off. The device wasn't applicated properly on the selected spot on the upper arm in 4/60 (6.7%) cases. 10/60 (16.7%) participants expressed concern about the device falling off and held on to it during blood collection. After pushing the button, 7/60 (11.7%) participants would have forgotten to start the timer. Assistance for checking the filling state of the test tube was needed in 10/60 (16.7%) cases. Many participants pointed out that they would have used a mirror if they had done the self-sampling at home. The study personnel had to intervene three times when devices (still connected with collection tube) were put on a flat surface with the risk of blood spilling out. Three participants needed assistance to remove and close the test tube. One of them pointed out the lack of strength and fine motor skills in her fingers due to rheumatoid arthritis. The test tube was shaken instead of slowly turned 5/60 (8.3%) times. Three participants had to be reminded of this step. 6/60 (10.0%) participants reported problems with the healing process. Five of them developed a scar, see In this study comparing capillary- and saliva-based self-sampling in IMID patients and HP we demonstrate that self-collection of capillary blood and saliva is feasible. Importantly, we also demonstrated that capillary blood produces interchangeable results to conventional venous blood. Participants reported high acceptance for self-sampling with a slight preference for capillary self-sampling. The majority in both groups preferred capillary self-sampling over traditional venous blood collection. Supervision of self-collection allowed the identification of pitfalls to improve the self-sampling approach.Importantly, we were able to demonstrate the interchangeability of capillary-based anti-SARS-CoV-2 antibodies, allowing precise home-based monitoring. These results are in line with a previous study that reported high correlation despite exposing samples to extreme shipping conditions  using a via regular mail, yet in contrast to HIV  of both devices and a statistically significant higher saliva SUS score. Compared to the previous RA study  with a m58/60 96.7%) were able to successfully collect capillary blood within first attempt. Medical education (HP) did not seem to have significant effect on success rate or correct completion of self-sampling steps. In a previous study evaluating a former version of the upper-arm device in patients with rheumatoid arthritis (RA), 16% of the patients needed a second attempt and 4% of patients failed to carry out the procedure . Usage of a validated composite approach  to invesSelf-collection of capillary blood and saliva is feasible and safe and could facilitate access to antibody testing of the general public. The interchangeability and high acceptance of capillary blood self-sampling enable flexible and convenient vaccine immunogenicity monitoring.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the Medical Faculty of the University of Erlangen-Nürnberg, Germany. The patients/participants provided their written informed consent to participate in this study.CS and JK wrote the draft manuscript. CS, JK, and E-TG performed the statistical analysis. All authors contributed to the article and approved the submitted version.The study was supported by the Deutsche Forschungsgemeinschaft  and Thermo Fisher Scientific . This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking . This study received funding from Thermo Fisher Scientific. The funder had the following involvement with the study: Provision of study materials, sample measurements and revision of the manuscript. The funder was not involved in the decision to submit the study for publication.Authors EV and LS were employed by Thermo Fisher Scientific Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Thermo Fisher Scientific. The funder had the following involvement with the study: Provision of study materials, sample measurements and revision of the manuscript. The funder was not involved in the decision to submit the study for publication.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Therefore, developing novel biomarkers based on CD8+ T cell can help evaluate the prognosis and guide the strategy of immunotherapy for patients with stage III LUAD. Thus, we abstracted twelve datasets from multiple online databases and grouped the stage III LUAD patients into training and validation sets. We then used WGCNA and CIBERSORT, while univariate Cox analysis, LASSO analysis, and multivariate Cox analysis were performed. Subsequently, a novel CD8+ T cell-related classifier including HDFRP3, ARIH1, SMAD2, and UPB1 was developed, which could divide stage III LUAD patients into high- and low-risk groups with distinct survival probability in multiple cohorts . Moreover, a robust nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its powerful predictive capacity. Besides, we detected the difference in immune cell subpopulations and evaluated the potential benefits of immunotherapy between the two risk subsets. Finally, we verified the correlation between the gene expression and CD8+ T cells included in the model by immunohistochemistry and validated the validity of the model in a real-world cohort. Overall, we constructed a robust CD8+ T cell-related risk model originally which could predict the survival rates in stage III LUAD. What’s more, this model suggested that patients in the high-risk group could benefit from immunotherapy, which has significant implications for accurately predicting the effect of immunotherapy and evaluating the prognosis for patients with stage III LUAD.Patients with stage III lung adenocarcinoma (LUAD) have significant survival heterogeneity, meanwhile, CD8 Lung cancer has become the first reason of all cancer-associated deaths worldwide, also in China, which accounts for nearly one million deaths each year , and appImmunotherapy has recently shown great efficacy for patients with stage III unresectable NSCLC, especially for those trapped in the lack of targetable mutations, who could not benefit from targeted therapy such as tyrosine kinase inhibitors (TKIs) , 9. As t+ T cell is the most important conductor in the cancer-immunity cycle and its activation and infiltration play a crucial role in immunotherapy  is essential nature in cancer progression. Alternatively, a variety of immune cell types within TME drive a fundamental environment that could respond to immunotherapy . Particuotherapy . Howeverotherapy . Therefophocytes , TP53-dehaustion , and higltration , whereasltration , 20. Nev+ T cell-related signature to estimate the prognostic stratification and the effect of immunotherapy in locally advanced LUAD. First, we integrated datasets about stage III LUAD, which were from multiple online databases. To identify the hub CD8+ T cell-related biomarkers, we then used weighted gene co-expression network analysis (WGCNA). Subsequently, we developed a novel CD8+ T cell-related classifier and constructed a robust nomogram to predict survival probability. Besides, the predictive performance was further validated in the multiple test sets. Moreover, we detected the difference in immune cell subpopulations and evaluated the potential benefits of immunotherapy between the two risk subsets. Finally, the valuation of this model was verified in a real-world cohort in evaluating immunotherapy efficacy. Taken together, it was expected that this CD8+ T cell-related model could contribute to predicting survival rates and accurately working out therapeutic strategies for locally advanced LUAD patients.Based on the rapid development of bioinformatics, in this study, we aim to establish a reliable CD8The gene expression profiles and clinical parameters of primary LUAD patients from 12 public cohorts were retrospectively analyzed, including 11 microarray datasets from the Gene Expression Omnibus (GEO) and Array-Express, and 1 RNA-Seq expression profile from The Cancer Genome Atlas (TCGA). Only patients who meet the following two criteria were included: i) detailed TNM staging information includes stage IIIA and IIIB according to the 7th edition of TNM classification of malignant tumors; ii) OS information includes follow-up time and survival status. The Combat algorithm was used to eliminate the batch effects. Then, the whole set was divided into training and internal validation cohorts in a ratio of 1:1 using stratified random sampling by caret R package. Another series from TCGA was used as the external validation cohort. The studies obtained from each of the databases are summarized together with series ID in https://cibersortx.stanford.edu/) to investigate the levels of 22 TIICs using the mRNA expression data of the training cohort. This online tool utilizes a deconvolution method to impute gene expression profiles and estimate the type and fractions of immune cells.We performed CIBERSORTx  for pathway and process enrichment analysis . Then, to further screen the prognosis of CD8+ T cell-related genes with the best predictive performance, the “glmnet” R package . We computed risk scores of each stage III LUAD patient and then divided them into high- and low-risk subsets according to the cutoff value of 28.401 determined via receiver operating characteristic (ROC) curve analysis using the R package “survminer”. Next, the Kaplan-Meier (KM) curve was performed to estimate the disparity in OS between low- and high-risk subsets by log-rank test. The prognostic ability of the CD8+ T cell-related classifier was explored with an analysis of the concordance index (C-index) and ROC curve. Then, we also used similar methods to verify the prognostic performance of the classifier constructed by the training cohort in the internal validation, external validation, and pooled validation cohorts.Univariate Cox regression analysis was performed to estimate the hazard proportions for genes of the highest correlation with CD8 package  was used+ T cell-related classifier could be an independent prognostic factor compared with other clinic factors for stage III LUAD patients in multiple cohorts. At last, risk score and three traditional clinical factors were used to generate the nomogram by using “rms,” “foreign,” and “survival” R packages. C-index, time-dependent ROC (t-ROC) curve and calibration plots of the nomogram for 1-, 3-, and 5-year OS plots were applied to elucidate the accuracy of actual observed rates with the predicted survival probability. The “timeROC” R package was utilized to perform the t-ROC analyses.Furthermore, based on univariate Cox regression and multivariate Cox regression analyses, we further confirmed whether the predictive performance of the CD8“pRRophetic version 0.5” to compute the half-maximal inhibitory concentration (IC50) of four common chemo-agents  in the training set (Tumor immune dysfunction and exclusion (TIDE) algorithms  and subcning set , 27. TheClinicopathological features and samples of stage III NSCLC patients were collected from January 2019 to December 2021 who received immunotherapy. After the screening by exclusion criteria, twenty-eight patients were enrolled as a validation cohort from real-world for analysis . What’s The Immunohistochemistry (IHC) was performed with a three-step method. After the dewaxing and hydrating, the tissue sections were boiled in autoclaved citric acid buffer (pH 6.0) for 20 min for antigen retrieval, and the peroxidase activity was quenched with 3% hydrogen peroxide for 15 min to avoid non-specific staining. Then, the sections were blocked for 15min followed by incubation overnight with CD8 antibody , anti-UPB1 antibody , HDGFRP3 antibody , SMAD2 antibody , or ARIH1 antibody  at 4°C. After that step, the sections were incubated with the secondary antibody at 37°C for 20 min. Subsequently, This step was followed by incubating with Horseradish Peroxidase for 20 min, and staining with 3,3- diaminobenzidine. At last, the sections were dehydrated and sealed after re-dyeing with hematoxylin. The IHC assays were performed by integral optical density (IOD) using Image J .P < 0.05 were retained for the next analysis. Two-tailed P < 0.05 was considered statistical significance.Software R (version 4.1.0) and GraphPad Prism (version 8.0.0) were applied to all data analyses. The Wilcoxon test and chi-square test were performed to assess the relationship between the risk score and clinical features. Survival analysis was utilized by the KM log-rank test. In the results of the CIBERSORT method, samples with  was selected . Besides, the samples of the training cohort were clustered by the average linkage and Pearson’s correlation values. Finally, a total of 18 modules were constructed by building a hierarchical clustering tree, where the gene set was independent as the tree branch. .To identify key modules correlated with CD8selected , B. B+ T cells  . To elucidate the potential function and mechanism of CD8+ T cells, we picked the yellow module as a hub module. Additionally, we got that these genes from the hub yellow module were mainly enriched in ubiquitin protein ligase binding, SMAD binding, and lymphocyte activation after GO and KEGG enrichment analysis .According to the criteria of the hybrid dynamic tree cut, we got that the yellow module was significantly associated with T cells, such as CD8= 0.002) . To eluc+ T cell-related genes were identified in the training cohort. Then these significant genes entered LASSO COX regression analyses  and multivariate Cox proportional risk regression analysis . Based on these analyses, the prognostic CD8+ risk model was constructed including the four most potential prognosis-related genes . The risk score = (1.078 × expression level of HDGFRP3+2.041 × expression level of ARIH1+3.079 × expression level of SMAD2-1.704 × expression level of UPB1) . Subsequently, all LUAD patients in the training cohort were then separated into low- and high-risk groups according to the cutoff value (28.401) . KM survival analysis showed that patients in the high-risk group were associated with a relatively poor OS than those in the low-risk group , while the heatmap and survival plot showed four prognostic expression profiles and survival status between two risk groups . Besides, univariate Cox regression analysis and multivariate Cox regression analysis demonstrated that the risk score could independently predict OS after adjusting for various clinicopathologic parameters in the training cohort . Moreover, ROC analysis of 5-year OS was applied to examine the predictive capacity of the CD8+ risk model, thus we got the 5-year AUC of risk model was 0.709, which was markedly higher than that of age (AUC = 0.548), gender (AUC = 0.506), and stage (AUC = 0.407), indicating that it had a more robust prediction of clinical outcome than the other clinical parameters .There were 805 hub genes within the yellow module selected for further analysis. After univariate Cox regression analysis on these hub genes, 88 significantly prognosis-associated CD8analyses  and multanalyses  and multanalyses  and multanalyses  and multanalyses  and multanalyses  and multanalyses  and mult. KM analysis indicated that patients in the high-risk group had a poorer prognosis compared to those in the low-risk group  and a low-risk group (n = 94) depending on the optimal risk cutoff value in the training cohort . KM anal. KM anal. KM anal+ T cell-related classifier in the external validation dataset. The optimal risk cutoff value in the training cohort was adopted to separate the external dataset into a high-risk group (n = 21) and a low-risk group (n = 53) . KM analysis also revealed that high-risk patients had a poorer prognosis than those in the low-risk group  . KM anal(n = 53) . KM anal(n = 53) . KM anal+ T cell-related classifier in the pooled validation dataset using the same methods. The external dataset was separated into a high-risk group (n = 116) and a low-risk group (n = 246) . KM analysis still revealed that high-risk patients had a poorer prognosis than those in the low-risk group  . KM analn = 246) . KM analn = 246) . KM analn = 246) . KM analn = 246) . KM anal+ T cell-related classifier in stage III LUAD patients, we analyzed the association of the signature with clinical variables in the training set. There was no significant association between the CD8+ T cell-related signature and TNM stage, gender, and age, apart from survival status . Similar significant findings were revealed in the internal test set and the pooled test set, except in subsets of age less than 65-year and female in the internal test set (P < 0.05). As for the external test set, we also observed that the risk scores were significantly associated with unfavorable clinical outcomes in the age, gender, patients with positive node metastasis, T3+4, and TNM stage subsets (P < 0.05). These findings suggested that our CD8+ T cell-related classifier has a promising clinical application for selecting high-risk patients.We next validate the prognostic ability of our CD8 subsets P < 0.005 subsets P < 0.005 subsets P < 0.005+ T cell-related classifier and three clinicopathological features shared in the training dataset and the other validation datasets, we developed a prognostic nomogram to predict the 1-, 3-, and 5- year OS probability of LUAD patients in the training dataset . The AUC points of the nomogram for 1-, 3-, and 5-year survival predictions were 0.719, 0.629, and 0.737, respectively . The C-index indicated that the nomogram had the highest predictive accuracy of survival than the other clinicopathological parameters . In addition, the calibration curves also confirmed a good consistency between predicted and observed scores in terms of probabilities of 1-, 3-, and 5-year OS . Similar results of calibration curves of nomogram were also found in the internal, external, and pooled validation datasets . Together, those findings indicated that our nomogram was suitable for clinical practice.By integrating the CD8 dataset . The AUC dataset . The AUC dataset . The AUC dataset . The AUC dataset . The AUC+ T cell-related classifier had closely and intrinsically connected with immune cells, which have a profound impact on predicting clinical outcomes and treatment efficacy, we further examined the difference and relationship of these immune cells with risk groups. The comparison of 22 immune cells between risk groups was displayed in a radar plot (+ T cells was remarkably higher in the low-risk group compared with those in the high-risk group (P = 0.049). We also found the fractions of other immune cells, including Dendritic cells (DCs) resting, and Tregs were significantly increased in low-risk patients , whereas the expression levels of Eosinophils and T cells CD4 memory activated were obviously higher in the high-risk group  .Since CD8= 0.050) –E.. To predict the response to immunotherapy, the TIDE algorithm was performed within different risk subsets. The result indicated that the TIDE score in the high-risk group was significantly lower than the low-risk group (P = 0.028) , including CTLA-4, LAG3, PDCD1, CD96, CD244, and CSF1R, etc. were significantly increased in the high-risk group, which could be promising immunotherapy targets . To pred. To predChemotherapy is another common therapy for stage III LUAD, while a higher IC50 value indicates resistance to the drug, otherwise, it is sensitive to the drug. The results showed that the IC50 values of cisplatin and gemcitabine decreased significantly in the high-risk subset; The IC50 values of docetaxel and paclitaxel had a decreased trend in the high-risk subset, although there was no significant difference –D; OverP = 0.0003; r = -0.7263, P < 0.0001), while the expression of UPB1 had a positive correlation with CD8 , which were consistent with the results obtained based on open databases (P > 0.05) (P = 0.043) , the degree of local tumor invasion, and the involvement of ipsilateral or contralateral mediastinal lymph nodes (N0-N3) , but (N0-N3) , however (N0-N3) . Consequ (N0-N3) , but due (N0-N3) , the PD- (N0-N3) . Because+ T cells by CIBERSORT and WGCNA analysis. Next, through univariate Cox analysis, lasso analysis, and multivariate Cox analysis, a CD8+ T cell-related signature including HDFRP3, ARIH1, SMAD2, and UPB1 was constructed. It was gratifying that this model could divide locally advanced LUAD patients into low- and high-risk groups with distinct overall survival in multiple cohorts . What’s more, in comparisons of the age, gender, and stage, the area under the ROC curve of the model was always the largest. Moreover, to make this model better applicable in the clinic, a nomogram including the traditional clinical parameters and risk signature was constructed. The ROC, C-index, and calibration curves validated its robust predictive capacity very well. Meanwhile, KM analysis revealed a significant difference in the subgroup analyses’ survival between the two risk subsets, especially in different TNM stages, suggesting the robust clinical application of our CD8+ T cell-related classifier. Finally, we confirmed that the high-risk group might benefit from immunotherapy or chemotherapy, and verified the valuation of this model in a real-world cohort, which further clarified the value of the model in predicting efficacy.Therefore, in this study, we extracted all RNA level profiles of these locally advanced LUAD patients from the GEO, Array Express, and TCGA database and divided these patients into the training, internal validation, external validation, and pooled validation cohorts firstly. Then we constructed the gene co-expression network through identified a significant yellow module as a hub module that exhibited great relevance to CD8+ T cells. In the follow-up mechanism exploration, whether UPB1 affects CD8+ T cells in LUAD by regulating the expression of ROS is a direction worth studying.Specifically, as the protective factor included in this model, the most important function of UPBEAT1 (UPB1) is that it could directly regulate the expression of a set of peroxidases which modulates the balance of reactive oxygen species (ROS) . Besides+ T cell, Gunderson et al. found that TGF-β increased the binding of Smad2 and reduced CXCR3 expression in CD8+ T cells, thereby limiting their trafficking into tumors  and TGF-β in the tumor immune microenvironment. For instance, Foxp1 interacted with Smad2/3 and suppressed the tumor-reactive T cells’ response to TGF-β in advanced tumors , were obviously decreased in the high-risk group. Owing to the unique capacity in initiation and regulation of T cell responses, DCs have been extensively explored as tools for immunotherapy, therefore it was convinced that a decrease in DCs is associated with poor prognosis  Conception and design: JF, HG. (II) Administrative support: HG. (III) Provision of study materials: JF, LX, SZ. (IV) Collection and assembly of data: JF, SZ. (V) Data analysis and interpretation: JF, LX. (VI) Experimental validation: JF, TZ, YY. (VII) Manuscript writing: All authors. (VIII) Final approval of manuscript: All authors.This work was supported by Interdisciplinary Training Program for Doctoral Candidate of Xi’an Jiaotong University (IDT1919), Guangdong Association of Clinical Trials (GACT)/Chinese Thoracic Oncology Group (CTONG) and Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant No.2017B030314120), Natural Science Foundation of Shaanxi Province (No.2019JM-559), and Xi’an Jiaotong University Free Exploration and Innovation Program (Student Category) (No.sxzy022021011).All authors would like to express our sincere thanks for sharing the online databases.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Background and objectiveThe isocitrate dehydrogenase (IDH) status of patients with World Health Organization (WHO) grade II or III astrocytoma is essential for understanding its biological features and determining therapeutic strategies. This study aimed to use radiological analysis to predict the IDH status of patients with lower-grade astrocytomas and to verify the pathological implications.MethodsIn this study, 47 patients with grade II (17 cases) or III astrocytomas (30 cases), based on 2016 WHO Classification, underwent methionine (MET) positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) on the same day between January 2013 and June 2020. The patients were retrospectively assessed. Immunohistochemistry showed 23 cases of IDH-mutant and 24 of IDH-wildtype. Based on fluid-attenuated recovery inversion (FLAIR)/T2 imaging, three doctors blinded to clinical data independently allocated 18 patients to the clear boundary group between the tumor and the normal brain and 29 to the unclear boundary group. The peak ratios of N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, and Cho/NAA and the tumor-to-normal region (T/N) ratio for maximum accumulation in MET-PET were calculated. For statistical analysis, Fisher’s exact test was used to assess associations between two variables, and the Mann-Whitney U test to compare the values between the IDH-wildtype and IDH-mutant groups. The optimal cut-off values of MET T/N ratio and MRS parameters for discriminating IDH-wildtype from IDH-mutant were obtained using receiver operating characteristics curves.ResultsThe unclear boundary group had significantly more IDH-wildtype cases than the clear boundary group (P<0.001). The IDH-wildtype group had significantly lower Cho/Cr (<1.84) and Cho/NAA (<1.62) ratios  and a higher MET T/N ratio  than the IDH-mutant group. The odds for the IDH-wildtype were 0.22 for patients who fulfilled none of the four criteria, including boundary status and three ratios, and 0.9 for all four criteria.ConclusionsThese results suggest that the combination of MRI, MRS, and MET-PET examination could be helpful for the prediction of IDH status in WHO grade II/III gliomas. Since the 2016 World Health Organization  Classification of tumors incorporated molecular diagnosis, the patients' isocitrate dehydrogenase (IDH) status has been clinically meaningful. For astrocytoma, the median survival of IDH-mutant (mut) and IDH-wildtype (wt) is 9.3 years and 1.9 years, respectively . On the Immunohistochemistry and sequence tests are the gold standards for examining IDH status. However, the detection of 2-hydroxyglutarate in magnetic resonance spectroscopy (MRS) has shown a 90% or higher sensitivity and specificity for predicting IDH-mut . This teThis study aimed to establish the method of radiological prediction of IDH mutational status using MRI, MRS, and MET-PET and to verify their pathological implications.This article was previously posted to the Research Square preprint server on May 26, 2022.Study populationWe retrospectively enrolled 75 patients who underwent MET-PET and MRS on the same day at our institution between January 2013 and June 2020. All patients had known pathological diagnoses of grade II or III astrocytomas, based on the 2016 WHO classification . Three nFrom this initial cohort of 75 patients, we sought to analyze only the outcomes of patients for whom all three reviewers or two of the three reviewers could agree on tumor classification. The physicians’ independent assessments aligned in 44 of the 75 (58.7%) cases studied. Specifically, their assessments aligned as follows: 14 patients were classified as being in the clear boundary group, and 30 patients were classified as being in the unclear boundary group. In 10 (13.3%) cases, two of the three physicians agreed on classification, and the remaining physician could not decide; of these 10 patients, the clinicians classified seven patients as being in the clear boundary group and three patients as being in the unclear boundary group. From this cohort of 54 patients, we further excluded seven patients with an unknown IDH status; thus, 47 cases  were ultimately included in our analysis, Of these 47 cases, we noted 17 cases of grade II astrocytoma and 30 cases of grade III astrocytoma, 23 cases of IDH-mut, and 24 cases of IDH-wt  was used, which was equipped with a three-dimensional acquisition system (Shimadzu Corporation) that provided 99 transaxial images at 2.65 mm intervals. The in-plane spatial resolution  was 4.8 mm, and the scans were conducted in three-dimensional mode. MET was injected intravenously at 3.5 MBq/kg through the cubital vein. During PET data acquisition, head position was corrected using laser beams projected onto ink marks drawn on the forehead, and images were reconstructed using an ordered subset expectation-maximization algorithm. Tracer accumulation in the region of interest (ROI) was analyzed using the standard uptake value (SUV), defined as the activity concentration in the ROI at a fixed time point divided by the injected dose and then normalized to the patient’s weight. The tumor-to-normal region (T/N) ratio of MET was calculated by dividing the maximum SUV of the tumor by the mean SUV of the contralateral normal frontal cortex. The ROI for the maximum tumor SUV was selected based on the areas with the highest tracer accumulation. The maximum tumor SUV was used instead of the mean SUV for the tumor to minimize the effect of tumor heterogeneity. Because of high and unexplained intersubjective SUV variability, we used the T/N ratio instead of the absolute SUV. Coregistration of PET and MRI was conducted using the Dr. View image analysis software package . In this study, PET and MRI fusion images are referred to as MET-PET.MRI2; matrix, 512×512); T2WI ; and FLAIR imaging . The slice thickness was 5 mm with a 1-mm slice gap. A gadolinium-based contrast agent, gadoteridol , was injected intravenously at 0.1 mL/kg body weight for contrast-enhanced studies which we performed with the same protocol as T1WI.We used the Achieva 3.0T TX QD MRI system  for transaxial T1-weighted imaging , 2200 ms; inversion time (TI), 950 ms; echo time (TE), 9.5 ms; field of view (FOV), 230 ×230 mmTo quantify the extent of the lesion, we measured the area of the hyperintense lesion on the FLAIR images and the area of the entire brain in the same cross-section using ImageJ software . We considered the area ratio as the spreading tumor ratio. For the T2/FLAIR image used for the reviewers’ assessment, we selected the same slice as the cross-section showing the highest accumulation of MET in the tumor on MET-PET.MRSProton MRS was conducted before injection of gadolinium agent simultaneously with conventional MRI using the single-voxel point-resolved spectroscopy technique with a TR of 2000 ms and TE of 288 ms. The total acquisition time required to obtain these parameters, including scanner adjustments, was <5 minutes. A cubic voxel with a side length of 2.0 cm was manually placed on the lesion, for which MET-PET showed the highest accumulation, avoiding hemorrhagic lesions and cysts as much as possible. Spectra were generated using an internal scanner software, thereby providing automatic peak assignment and ratio calculations. The NAA/ Cr, Cho/Cr, and Cho/NAA peak ratios were recorded. Cr was used as the benchmark.Pathological evaluationWe prepared formalin-fixed paraffin-embedded sections of labeled tissue for histology. The specimens underwent hematoxylin & eosin (H&E) staining and immunohistochemistry to determine IDH status using an anti-IDH1R132H monoclonal antibody . Immunoreaction was considered positive when the tumor cells showed strong and diffuse staining for IDH1R132H.The specimens were also stained with an anti-Ki-67 antibody  to evaluate tumor proliferation. The Ki-67 labeling index was visually quantified by counting the number of immunopositive nuclei in areas with the highest Ki-67 immunoreactivity as the percentage of Ki-67 positive cells per 1000 tumor cells.The antigen was retrieved in an autoclave (121°C for 15 min). We used the Envision kit (Dako) as a source of secondary antibodies and 3,3-diaminobenzidine as the chromogen.Statistical analysisWe used Fisher’s exact test to assess associations between IDH status and sex, WHO grade, and tumor-brain boundary status, and the Mann-Whitney U test to compare the spreading tumor ratio, MRS parameters, MET T/N ratio, and Ki-67 labeling index between IDH-wt and IDH-mut groups. Statistical significance was set at P<0.05. Receiver operating characteristic (ROC) analysis was conducted to determine the optimal thresholds of the Cho/Cr, Cho/NAA, and MET T/N ratios for discriminating IDH-wt from IDH-mut and the area under the curve. We determined the optimal sensitivity and specificity from the highest sum of the receiver operating characteristics (ROC) curve. R software  was used for all statistical analyses.The spreading tumor ratios were significantly higher for the unclear boundary group than for the clear boundary group , but the correlation between boundary status and tumor grade was not significant (P=0.99). Therefore, this finding confirmed that our reviewers’ imaging-based classifications of the clear and unclear boundary groups were objectively different. Spreading tumor ratios were significantly higher for the IDH-wt group (20.7±14.0) than for the IDH-mut group (11.1±8.4) (P=0.008) ; however, the NAA/Cr ratio and Ki-67 labeling index showed no significant difference between the two groups  no match, (2) one match, (3) two matches, (4) three matches, and (5) matches for all four items were 22.2%, 0%, 40.0%, 87.5%, and 90.0%, respectively (Figure Representative casesCase 1A 19-year-old man presented with convulsions. MRI revealed a brain tumor in the right frontal lobe, which showed low signal intensity on T1WI and high signal intensity with clear boundaries on T2WI Figure  and FLAICase 2A 70-year-old woman experienced gait disturbance due to bilateral lower-limb weakness. MRI showed a nonenhanced tumor bilaterally spreading to the periventricular white matter with unclear boundaries Figure  and 3H. Several reports exist regarding the prediction of IDH-mut based on detecting 2-hydroxyglutarate ,12,13. IThis study revealed more IDH-wt lower-grade astrocytic gliomas in the unclear boundary group than in the clear boundary group. This finding was supported by the significantly higher spreading tumor ratio in the IDH-wt group than in the IDH-mut group. By contrast, Baldock et al. analyzed the MRIs of 172 patients with contrast-enhanced gliomas and found no significant difference in the dispersal index between the IDH-wt and IDH-mut groups . HoweverGliomatosis cerebri was excluded from the 2016 WHO classification because it comprises a genetically and epigenetically heterogeneous group of diffuse gliomas . We encoHistological findings of necrosis and microvascular proliferation are reportedly more frequent in gliomatosis cerebri with IDH-wt than those with IDH-mut . HoweverIn the present study of lower-grade astrocytoma with marked invasiveness, the Cho/Cr and Cho/NAA ratios were significantly lower for IDH-wt cases. Cho is a metabolic marker of cell membrane density and integrity  and may NAA is an internal neuronal marker that parallels the relatively constant neuronal cell density . One repThe cut-off values for the Cho/Cr, Cho/NAA, and MET T/N ratios for differentiating IDH-wt from IDH-mut cases in the present series obtained by ROC analyses were 1.84, 1.62, and 1.44, respectively. However, these values were obtained from a limited series; therefore, care must be taken to ensure versatility and reproducibility. A higher diagnostic rate of IDH-wt was associated with meeting more of the following criteria: Cho/Cr ≤1.84, Cho/NAA ≤1.62, T/N ratio ≥1.44, and an unclear boundary (Figure The WHO Classification of Tumors, 5th edition , was relA decrease in the Cho/Cr or Cho/NAA ratio in an intra-axial brain tumor with unclear boundaries suggests IDH-wt. Due to its high infiltration, the Cho/Cr ratio is likely underestimated because the tumor cell density is sparse in an IDH-wt tumor. However, we considered that the neuronal cells in the IDH-wt group had a lower Cho/NAA ratio than those in the IDH-mut group. Although our study does not correspond to the new WHO classification, 5th edition, the pathological infiltrative nature of IDH-wt could be supported by MRS and MET-PET information. This diagnostic method of combining MRI, MRS, and MET-PET helped predict the IDH status of lower-grade astrocytoma. We need to verify this method's prediction of IDH status through a future prospective study using the new WHO classification, 5th edition.


Background: Sequencing quality has improved over the last decade for long-reads, allowing for more accurate detection of somatic low-frequency variants. In this study, we used mixtures of mitochondrial samples with different haplogroups  to investigate the applicability of nanopore sequencing for low-frequency single nucleotide variant detection.Methods: We investigated the impact of base-calling, alignment/mapping, quality control steps, and variant calling by comparing the results to a previously derived short-read gold standard generated on the Illumina NextSeq. For nanopore sequencing, six mixtures of four different haplotypes were prepared, allowing us to reliably check for expected variants at the predefined 5%, 2%, and 1% mixture levels. We used two different versions of Guppy for base-calling, two aligners , and three variant callers  to compare low-frequency variants. We used F1 score measurements to assess the performance of variant calling.Results: We observed a mean read length of 11 kb and a mean overall read quality of 15. Ngmlr showed not only higher F1 scores but also higher allele frequencies (AF) of false-positive calls across the mixtures  compared to Minimap2 . Mutserve2 had the highest F1 scores  across all callers and mixture levels.Conclusion: We here present the benchmarking for low-frequency variant calling with nanopore sequencing by identifying current limitations. While next-generation sequencing (NGS) allowed cost-effective sequencing of whole-genome sequences over the last decades, third-generation sequencing (TGS) has finally made it possible to generate the first complete assembly of a human genome . The implong-read-tools.org , while for SNP and variant analysis, 109 tools/pipelines are currently available. In contrast to the high number of available tools, only six comparative studies are presently available that benchmark long-read data analysis tools/platforms   and TGS data with 11 different variant callers in mixtures down to 10%, however limiting the TGS tools for germline variant calling with DNAseq and GATKs HaplotypeCaller. Thereby for the somatic haplotype calling, only NGS data was used for benchmarking purposes.Numerous available tools for TGS are tracked on ools.org , currentlatforms . In factet al. present protocols for mtDNA analysis on ONT devices and results with error rates of 0.3% per mtDNA sequence–corresponding to 50 base-substitution errors per sample   .In this study, we deeply investigate low-frequency variants calling for long-read data, as it is of great importance to investigate heteroplasmy and somatic variants accurately. First, we prepared a gold standard from NGS data of mitochondrial genomes by merging the results from three different variant callers. Subsequently, the TGS mixtures were prepared, base-called, trimmed, and additional quality control (QC) was performed. Finally, the performance of two variant callers designed for short-read data and one low-level variant specific for long-read data was compared. We evaluated the performance of the callers to detect low-frequency variants in mtDNA mixtures at predefined 5%, 2%, and 1% levels. In different short-read sequencing-based studies, the sensitivity thresholds to detect heteroplasmy ranged from >10% to 0.1% . In addiAll DNA was extracted from blood with the Blood and Cell Culture DNA Midi kit (Qiagen). Four samples  were used in this study for the mixture models. The selected samples were from participants of European descent . We haveMtDNA was amplified by two overlapping long-range PCRs, which were subsequently tagmented and sequenced on an Illumina NextSeq. Deep mitochondrial sequencing was performed with bait enrichment or long-range PCR, two primer sets used for the long-range PCR were: MTL-F1 5′- AAA GCA CAT ACC AAG GCC AC -3′, MTL-F2 5′- TAT CCG CCA TCC CAT ACA TT -3′, MTL-R1 5′- TTG GCT CTC CTT GCA AAG TT -3′, MTL-R2 5′- AAT GTT GAG CCG TAG ATG CC -3’. Next-generation sequencing was performed on the NextSeq500  to produce 2 × 150 bp reads. Raw sequencing reads were converted to standard FASTQ format using bcl2fastq software 2.17.1.14 . Raw FASTQ files were analyzed with FastQC and aligned with BWA MEM to the mitochondrial reference genome (rCRS). The resulting SAM files were processed with SAMtools to sorted BAM files. Subsequently, the BAM files were processed with GATK, and duplicates were removed with MarkDuplicates. Additional quality control was performed with QualiMap2 and afterward, MultiQC was applied to the resulting reports, including the FastQC reports.https://github.com/rrwick/Filtlong) to only include the best 50% of the reads, based on Phred quality scores (q-score), with a minimum read length of 9 kb. Then, the reads were trimmed with NanoFilt (v2.5.0)   and 75bp(v2.5.0)  and Ngml(v2.5.0) . Finally(v2.5.0) .query and subsequently merged into consensus variants  Common Variants are homoplasmic variants found in both samples from a mixture.2) Major Variants are all variants found in the major contribution of a mixture, expected to be present at 99%, 98%, and 95% in the 1%, 2%, and 5% mixtures, respectively, further including private mutations at lower levels. 3) Minor Variants are all variants found in the minor contribution of the mixture, expected to be present at 1%, 2%, and 5%. The performance of two variant callers designed for short-read data (i.e. Mutserve2 and Freebayes) and one low-level variant specific for long-read data (Nanopanel2) was compared. We chose Mutserve2 , as the In order to assess the applicability of nanopore long-read sequencing for low-frequency variant calling, three software tools, chosen based on various strengths and weaknesses, were compared  . The ali1 score was calculated (F1 score = 2 × (sensitivity × precision) ÷ (sensitivity + precision)). For calculating the F1 score and the determination of false-positive calls, we used the following minimum variant level to filter the output of Mutserve2, Freebayes, and Nanopanel2: ≥0.025 for the 5% mixture, ≥0.015 for the 2% mixture, and ≥0.007 for the 1% mixtures. In addition, to assess the variant calling performance, the number of false positives (nFP) was calculated.To measure the variant calling performance, we stratified the detected variants from the nanopore data as false-positive, true-positive, and false-negative calls. True-positive variants, thus, variants that we expected to be present in the mixtures, were determined with the previously generated Illumina short-read gold standard. On the other hand, false-positive variants are variants that were not identified in the NGS gold standard. False-negative variants were expected to be present in the mixtures but were not detected. Then the FThe NGS gold standard was derived as previously formulated  by sequeAfter deep-nanopore sequencing of the mtDNA long-range PCR products, we obtained a mean read q-score of 13.7 and a mean read length of 8.6 kb (SD = ±247.6 bp) across all raw sequencing data, base-called with the Guppy super-accurate model . After t1 score, the number of false-positive calls (nFP), and the number of detected minor (i.e. low-frequency) variants. The common, major, and minor variants expected in the mixture were determined using a previously derived Illumina short-read gold standard. Besides the variant caller, the utilized aligner affected the outcome. First, Ngmlr showed a better performance when used in combination with Mutserve2 with a mean F1 score of 0.83 and a total of 34 nFP across all mixtures and levels . Second, when variants were called with Freebayes, Minimap2  performed better compared to Ngmlr . Lastly, for the variant detection with Nanopanel2, Ngmlr  showed the slightly better performance compared to Minimap2 .For the comparison, we focused on the Fd levels . Minimap1 scores but the same nFP  compared to mixture II   and lowest nFP  across all callers and mixture levels. The lower F1 scores in mixture II was mainly driven by the overall lower AF of the minor variants of the 2% mixture  . Mutserv mixture . The det the nFP .FP and therefore, lower F1 scores. However, we obtained higher F1 scores and lower nFP in the mixture I  compared to mixture II   .1 scores compared to Mutserve2, which was due to the low number of true-positive calls. Compared to Mutserve2 and Freebayes, Nanopanel2 did not detect all common and major variants. Nanopanel2 performed better with Ngmlr and detected lower nFP compared to Freebayes. On the other hand, the number of true-positive variant calls detected with Nanopanel2 was significantly lower compared to the other two callers .Lastly, Nanopanel2 requires nanopore data from an older Guppy version. Nanopanel2 also showed lower F callers  as well.After removing all expected variants from the results, we further analyzed Type I errors in more detail. Here, we focused on the Q20 filtered data for both mixtures, and both mappers were analyzed with Mutserve2 only. We removed variants around 3107 and 302–316 due to reference issues. A total of 189 false-positive (FP) variants could be observed in the 12 samples (95 FPs with Minimap2 and 94 FPs with Ngmlr) greater or equal to the 0.7% level. Ngmlr yielded the highest false-positive variant levels at 17.4% on position 70 , whereas Minimap2 showed a 6.2% false-positive variant on position 6991 . For both mixtures, I and II, the expected numbers of variants are 55 and 43, respectively. Applying a different threshold per predefined mixture level, the cut-off levels were set at 0.7%, 1.5%, and 2.5% to interpret the 1%, 2%, and 5% mixtures, respectively, with Mutserve2 . We obsep = 2.2e−16). Both aligner/mapper had the lowest variant levels around 97% in the mixture I, while all common variants in mixture II were above 98% , phantom mutations, and sequencing quality. These NGS artifacts could result in Type II errors in this study. To counteract systematic artifacts arising from NGS variants calling, three different callers were used and only variants detected with at least two tools were included in the gold standard.Our results show that all steps of the data processing pipeline are of importance and can affect variant calling. With the new Guppy five super-accurate base-calling, we obtained overall higher base-calling q-scores compared to the older version 4. In addition, we detected fewer false-positive calls with a higher q-score , though 1 scores with Mutserve2 as the subsequent caller. In contrast, alignment with Minimap2 better reflected the expected variant levels of the common variants and led to lower variant levels among the false-positive calls. We also observed some “phantom-mutation” with higher levels for Ngmlr, compared to Minimap2, where we could identify homopolymorphic nucleotide stretches as the main cause. In addition to the aligner, the reference sequence could impact the variant calling. However, changing the reference to the major haplogroup would only be beneficial for detecting the major variants expected at 95%–99% depending on the mixture ratios from 5% down to 1%, which were all detected already. Thus, we would not expect an increase in the F1 score, as the detection of minor variants would not benefit from this approach.We demonstrate the strengths and limitations of each mapper/aligner and variant caller pairs. For example, alignment with Ngmlr led to fewer false positives and higher F1 scores. Using Freebayes, significantly more false positives were detected in comparison with the other two tested callers. In contrast, the lower F1 scores obtained with Nanopanel2 were due to the low number of true-positive variant calls, also including missing common and major variants. As Nanopanel2 is incapable of processing nanopore data from the super-accurate mode, this lower quality base-calling could be reflected in the lower F1 scores. As previously mentioned, the majority of false-positive variants calls were located within homopolymer stretches, which are known to be especially prone to systematic nanopore sequencing errors  have been challenging to delineate with short-read sequencing , especia1 score of the 2% level of mixture I is more reflective for the future sequencing of real samples. One limiting factor of our study was that we could not compare all somatic variant callers. However, we do provide all data so that other researchers can compare different variant callers or methods in the post-processing. Another tool exclusively designed for nanopore sequencing data is Medaka, which creates a consensus sequence and calls variants (https://github.com/nanoporetech/medaka). However, using Medaka, we only detect major and common variants in our data set and no minor variants (low-frequency variants)  , therefoariants) . For botariants) . NeverthIn conclusion, our data show the feasibility of nanopore long-read low-frequency variant calling. However, the number of Type I and II errors increases between the 2% and 1% levels significantly. Here, the rather low per-base quality filtering (compared to current NGS data) of Q20 yields a higher number of false-positive variants, especially for variant callers designed for NGS data. Therefore, novel computational approaches are needed to disregard artifacts by taking advantage of almost complete haplotypes, as is the case with the short 16.6 kb mtDNA. Overall, we showed that all post-processing steps affect the final results, from base-calling and over aligning/mapping to variant calling tools. As further improvements in the data analysis pipeline arise and novel tools will become available for the mentioned steps, we herein provide a novel TGS benchmarking dataset.